We aimed to evaluate the intervention effect of risk-stratified nursing mode and digital health management on pregnant women with gestational diabetes mellitus (GDM) and hypertensive disorders of pregnancy (HDP). A prospective randomized controlled trial was conducted to recruit 120 GDM patients with HDP. A control group (n=60) and an intervention group (n=60) were established for random allocation. Both groups received conventional nursing, and the intervention group additionally received risk-stratified nursing by a mobile health platform throughout the third trimester of pregnancy. The intervention group had significantly lower preterm delivery rate, macrosomia incidence rate, and fetal distress incidence rate than those of the control group (P<.05). The cesarean section rate and maternal complication rate significantly decreased in the intervention group in contrast to those in the control group (P<.05). Before delivery, the intervention group outperformed the control group in the levels of fasting blood glucose, postprandial blood glucose (PBG), systolic blood pressure, and diastolic blood pressure (P<.05). The risk-stratified nursing mode plus digital health management can effectively ameliorate the blood glucose and blood pressure control in pregnant women with GDM and HDP.

Children undergoing intravenous procedures often experience substantial fear and discomfort, which can make these procedures more difficult to perform. This study examined whether virtual reality could serve as a supportive technology during pediatric intravenous procedures. A randomized, parallel-group design was used with 50 children assigned to either a virtual reality group or a comparison group. Pain and fear were assessed using self-report measures, and physiological responses were evaluated using heart rate variability, heart rate, and blood pressure indicators. Children in the virtual reality group reported significantly lower fear than those in the comparison group, whereas pain scores did not differ significantly between groups. The virtual reality group also showed significant differences in heart rate variability-related indices, suggesting a less pronounced sympathetic-dominant autonomic pattern, which may be clinically meaningful in the context of lower fear during intravenous procedures. These findings support the potential role of VR as a supportive technology in pediatric nursing care during intravenous procedures.

Tracheostomy care is a major skill that requires knowledge, clinical reasoning, and competency. Simulation-based education offers a safe learning environment; however, evidence comparing virtual and standardized patient simulations in tracheostomy care remains limited. The study compared the effects of virtual and standardized patient simulations on nursing students' knowledge, knowledge retention, clinical reasoning, and competency in tracheostomy care. A randomized, 2-arm, parallel-group, pre-test and post-test study was conducted with 68 senior nursing students (virtual patient group:34, standardized patient group: 34). Both groups received identical theoretical training and participated in simulation scenarios. The "Tracheostomy Care Knowledge Test", "Clinical Reasoning Scale", and "Nursing Student Competency Scale" were used for pre-test and post-test. Knowledge retention was assessed 4 weeks after the simulation with the "Tracheostomy Care Knowledge Test". No significant differences were found between groups in baseline measurements (P>.05). Both groups demonstrated significant improvements in knowledge, clinical reasoning, and competency (P<.05). Post-test and 4-week retention scores on the "Tracheostomy Care Knowledge Test" were significantly higher in the standardized patient group, with large effect sizes. "Clinical Reasoning Scale" scores increased significantly in both groups (P<.05). The standardized patient group achieved significantly higher post-test scores on the "Verification of Clinical Problems" subscale (P<.05). Regarding "Nursing Student Competency Scale", significant improvements across all subscales and total scores were observed only in the standardized patient group (P<.05). Both virtual and standardized patient simulations are efficacious simulation-based education strategies in tracheostomy care. Nevertheless, standardized patient simulation seems to be more efficacious in enhancing knowledge, knowledge retention, clinical reasoning, and competency.

Changes and transformations in the field of technology significantly affect nursing education. Nursing students are expected to actively engage with a variety of advanced technological tools throughout their education to develop technological competence. This exploratory qualitative study aimed to explore nursing students' experiences and perspectives regarding collaborative technology-based assignments included in the "technology use in nursing" course, as well as their perceived contributions to personal and academic development. The study was conducted with 22 nursing students selected using purposive sampling. The findings revealed 3 main themes reflecting students' experiences: "Personal and academic development through technology-supported learning," "Enhanced awareness and integration of technology into nursing education and practice," and "Opportunities and challenges of AI use." In addition, a total of 10 subthemes were identified. Participants perceived that collaborative technology-based assignments supported their active engagement, experiential learning, technological awareness, and personal and academic development.

The study aims to examine the effect of progressive relaxation exercises administered to nursing students through telenursing on sleep quality after the February 6 earthquake in Turkey. The randomized controlled study included 86 nursing students from 2 universities in eastern Turkey, residing in one of the 11 cities affected by the February 6 earthquake. Experimental group received progressive relaxation exercises for 20 minutes every night for 4 weeks by making a video call with the student. Data were collected using a personal information form and the Pittsburgh Sleep Quality Scale. Although there was no significant difference between the pre-test PSQI mean scores of the experimental group and the control group (P>.05), a significant difference was observed between their post-test mean scores (P<.005). The research results showed that progressive relaxation exercises can be an effective intervention in improving sleep quality after an earthquake. In addition, conducting these exercises through telenursing contributed significantly to the sustainability and control of the intervention.

Neonatal seizures are high-risk clinical events that require rapid recognition but present subtle symptoms. Although simulation-based education supports clinical competence, the use of escape-room-based approaches in neonatal nursing education remains underexplored. The aim of the study was to explore the experiences of nursing students regarding escape room-based simulation for managing neonatal seizure scenarios. This descriptive qualitative study involved 8 undergraduate nursing students engaged in a 5-stage escape-room simulation using a high-fidelity neonatal simulator (Super Tory S2220, Gaumard Scientific), which included seizure assessment, cause identification, intervention selection, information interpretation, and communication. Data were collected through semistructured focus group interviews and researcher observations. Qualitative data were analyzed using Giorgi's descriptive phenomenological approach to identify essential themes. Five themes and 12 subthemes were organized across 5 experiential dimensions- environmental, cognitive, behavioral, relational, and value. These included authentic immersion within a high-stakes environment (environmental), practical expansion of theoretical knowledge (cognitive), strengthening clinical competence and enhancing self-efficacy (behavioral), collaborative team dynamics and multifaceted communication competence (relational), and professional self-reflection and formation of responsibility (value). As a promising gamified educational modality, escape-room-based simulations provide valuable insights into how students experience the enhancement of technical proficiency, clinical judgment, and teamwork, offering a valuable framework that prepares nursing students for complex, real-world neonatal emergency challenges.

Accurate and timely mortality prediction is essential for nursing clinical decision-making in intensive care units (ICUs). Although the Sequential Organ Failure Assessment (SOFA) score is widely used to evaluate organ dysfunction, its manual calculation limits routine application in fast-paced clinical environments. This study aimed to enhance ICU system-level safety and workflow efficiency by refining and evaluating an automated Electronic Medical Record (EMR)-integrated SOFA scoring system (AI-SOFA) to evaluate: (1) its predictive performance for mortality compared to traditional manual scoring; and (2) its clinical utility as a nursing informatics initiative. A retrospective cohort study was conducted using EMR data from 2559 ICU admissions at a tertiary hospital in South Korea. Automated SOFA scores were generated using 11 routinely collected clinical parameters. Logistic regression, random forest, and XGBoost models were trained, and model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, and F1 score. ICU mortality increased markedly with higher SOFA scores, exceeding 50% at scores ≥13. Among the machine learning (ML) models, XGBoost demonstrated the highest predictive performance (AUROC=0.9005), outperforming random forest (0.8975) and logistic regression (0.8722). In contrast, mortality prediction based on manual SOFA scoring showed substantially lower accuracy (AUROC=0.64). The AI-SOFA system serves as a nursing informatics tool that supports nursing workflows by enabling real-time risk stratification, reducing documentation burden, and facilitating timely clinical decision-making in ICU settings.

Social determinants of health (SDOH) may influence hospital readmissions. The purpose of this project was to apply provider-assigned International Classification of Diseases-10-CM codes to the Healthy People 2030 SDOH Framework for use in future research focused on hospital readmissions using the 2018 Nationwide Readmissions Database (NRD). We classified all subcodes codes ranging from Z55 to Z65 that were captured within the 2018 NRD. Three researchers independently categorized 80 unique codes into the 5 Healthy People 2030 domains. Inter-rater reliability was 0.664 and the Kappa statistic was 0.54 for initial classifications. The Kappa statistic shows moderate agreement between the raters. After robust discussion, we adjusted 33 classifications to reach classification consensus. Among the 80 codes classified, 8 (10%) were classified into Economic Stability, 6 (7.5%) into Education Access and Quality, 0 into Healthcare Access and Quality, and 13 (16.3%) into Neighborhood and Built Environment, and 53 (66.3%) into Social and Community Context. Classification challenges were due to overlapping and related features of the descriptions that could be applied to more than one domain. Clear coding practice will support researchers, case managers, social workers, and providers in partnering with patients and their family to negotiate a safe evidence-based discharge transition plan.

This study addressed the unique challenges of promoting physical activity among older adults with chronic diseases in the informal workforce, who often face physical limitations and lack structured schedules or access to workplace wellness programs. To address this gap, a mobile application based on Social Cognitive Theory was developed to encourage physical activity among older adults with hypertension. Guided by the ADDIE model, the app's development included a needs assessment involving a survey of 90 individuals and a literature review. This informed the design of key features, such as educational content, a physical activity calendar, exercise location identification, a ranking and rewards system, and a discussion forum. Rigorous testing was conducted, including pilot testing (n=6), usability testing (n=90), and content validation by experts (n=5). The app was well-received by older adults with hypertension (n=90, mean age=66.7 y, 82% female), who particularly valued features promoting self-care and information on the benefits of physical activity. This theoretically driven app offers a promising solution to promote physical activity in this vulnerable population. Further research will evaluate its effectiveness and explore its wider applicability.

This pilot study explored the longitudinal relationship between daily dementia caregiving and physiological stress, assessed through heart rate variability (HRV) measured using a wearable device, among Korean American home care workers (HCWs). Fifteen HCWs wore the Ōura ring for 21 days, providing 14 days of valid nocturnal HRV data after baseline calibration. HRV was quantified as the root mean square of successive differences (RMSSD) derived from photoplethysmography (PPG)-based interbeat intervals. To minimize interindividual variability, HRV values were standardized within participants. Multilevel linear models examined within- and between-person associations of dementia caregiving (yes or no and total hours) with HRV, adjusting for covariates. All were women, and the majority of HCWs (86.96%) were unsure about the type of dementia their care recipients had. Of 210 potential daily observations, 198 (94.3%) were valid. HRV displayed substantial within-person day-to-day fluctuations (intraclass correlation = 0.20) despite relatively low baseline self-reported stress. Although dementia caregiving was not significantly associated with HRV, the estimated coefficients suggested small-to-moderate within-person effect sizes. These findings highlight the dynamic nature of physiological stress and underscore both the feasibility and limitations of nocturnal PPG-derived HRV for real-world stress assessment. The study provides preliminary methodological guidance for wearable-based HRV research in understudied caregiver populations.

This study evaluated the long-term effects of the ControlVit mobile application on clinical outcomes and patient empowerment in individuals with chronic heart failure (HF), 6 months after discontinuation of the app. In this open-label, randomized, controlled clinical trial, patients were assigned to either an intervention group receiving standard HF management plus the ControlVit app for 6 months or to a control group receiving standard care alone. After 6 months, app use was discontinued, and all participants were followed for an additional 6 months. The primary outcome was the composite of death or HF-related hospital readmission at 12 months, whereas secondary outcomes included changes in body weight, natriuretic peptide levels, New York Heart Association (NYHA) functional class, and patient empowerment, measured using the Empowerment Evaluation Scale (EES). A total of 140 patients were enrolled (28.6% women; mean age, 67 y). At 12 months, 8 patients (11.4%) in the intervention group and 3 patients (4.3%) in the control group experienced the primary outcome [relative risk (RR): 2.62; 95% CI: 0.73-9.52; P=.122). Empowerment levels were significantly higher among app users compared with controls (EES score: 4.68 ± 0.36 vs. 4.40 ± 1.91; P=.012). Use of SGLT2 inhibitors and ARNi increased significantly in both groups during follow-up (ControlVit: 60% to 85%, P<.001; Control: 51.4% to 75%, P<.001). Among outpatients with chronic HF participating in a structured HF program, the addition of a mobile telemonitoring application did not reduce mortality or HF readmissions 6 months after its discontinuation. However, the intervention was associated with sustained improvements in patient empowerment, suggesting a lasting behavioral impact beyond the period of active app use.

This case-control study develops a machine-learning (ML)-based fall-risk-prediction model for military-hospital inpatients, using electronic records (January 2020 to December 2023) from 11 military hospitals in South Korea. The dataset includes 1470 fall and 3478 nonfall cases, and 25 predictive factors are selected based on data completeness and previous studies. ML models, including logistic regression, decision trees, random forest (RF), and gradient boosting, are developed and evaluated using 10-fold cross-validation. The predictive performances of these models are compared with that of the Morse fall scale (MFS). RF exhibits superior performance, with accuracy, recall, F1-score, and area under the curve (AUC) of 0.76, 0.77, 0.76, and 0.83, respectively. The MFS exhibits a poor predictive capability of 0.02. The key predictive variables include the admission method (on foot), medical department (orthopedics), MFS score, activity status, and body mass index. The findings reveal the limitations of MFS and higher accuracies of ML models in predicting falls among young military inpatients, highlighting the suitability of ML models for improving fall-prevention strategies. Future research should integrate real-time patient conditions and key fall-related factors into prediction models and embed these into clinical decision-making systems.